SoroushVahidi/scidocs huggingface dataset:
Power Converter EMI Analysis Including IGBT Nonlinear Switching Transient Model
(2005)

It is well known that very high dv/dt and di/dt during the switching instant is the major high-frequency electromagnetic interference (EMI) source. This paper proposes an improved and simplified EMI-modeling method considering the insulated gate bipolar transistor switching-behavior model. The device turn-on and turn-off dynamics are investigated by dividing the nonlinear transition by several stages. The real device switching voltage and current are approximated by piecewise linear lines and expressed using multiple dv/dt and di/dt superposition. The derived EMI spectra suggest that the high-frequency noise is modeled with an acceptable accuracy. The proposed methodology is verified by experimental results using a dc-dc buck converter
Large scale predictive process mining and analytics of university degree course data
(2017)

For students, in particular freshmen, the degree pathway from semester to semester is not that transparent, although students have a reasonable idea what courses are expected to be taken each semester. An often-pondered question by students is: "what can I expect in the next semester?" More precisely, given the commitment and engagement I presented in this particular course and the respective performance I achieved, can I expect a similar outcome in the next semester in the particular course I selected? Are the demands and expectations in this course much higher so that I need to adjust my commitment and engagement and overall workload if I expect a similar outcome? Is it better to drop a course to manage expectations rather than to (predictably) fail, and perhaps have to leave the degree altogether? Degree and course advisors and student support units find it challenging to provide evidence based advise to students. This paper presents research into educational process mining and student data analytics in a whole university scale approach with the aim of providing insight into the degree pathway questions raised above. The beta-version of our course level degree pathway tool has been used to shed light for university staff and students alike into our university's 1,300 degrees and associated 6 million course enrolments over the past 20 years.
Bethe-ADMM for Tree Decomposition based Parallel MAP Inference
(2013)

We consider the problem of maximum a posteriori (MAP) inference in discrete graphical models. We present a parallel MAP inference algorithm called Bethe-ADMM based on two ideas: tree-decomposition of the graph and the alternating direction method of multipliers (ADMM). However, unlike the standard ADMM, we use an inexact ADMM augmented with a Bethe-divergence based proximal function, which makes each subproblem in ADMM easy to solve in parallel using the sum-product algorithm. We rigorously prove global convergence of Bethe-ADMM. The proposed algorithm is extensively evaluated on both synthetic and real datasets to illustrate its effectiveness. Further, the parallel Bethe-ADMM is shown to scale almost linearly with increasing number of cores.
Using a Programmable Toy at Preschool Age: Why and How?
(2008)

Robotic toys bring new dimension to role-play activities in kindergarten. Some preschool curricula clearly identify reasons for their inclusion. However, preschool teacher needs to revise usual teaching methods in order to use them. Offering a programmable toy or robotic-related activity doesn't mean immediate success in work with children. We document our research with concrete programmable device in a preschool classroom. Details of robotic-related sessions can help reader to design the quality game for preschool-age based on using a programmable toy.
Design Space Exploration of Network Processor Architectures
(2002)

We describe an approach to explore the design space for architectures of packet processing devices on the system level. Our method is specific to the application domain of packet processors and is based on (1) models for packet processing tasks, a specification of the workload generated by traffic streams, and a description of the feasible space of architectures including computation and communication resources, (2) a measure to characterize the performance of network processors under different usage scenarios, (3) a new method to estimate end-to-end packet delays and queuing memory, taking task scheduling policies and bus arbitration schemes into account, and (4) a evolutionary algorithm for multi-objective design space exploration. Our method is analytical and based on a high level of abstraction, where the goal is to quickly identify interesting architectures, which may be subjected to a more detailed evaluation, e.g. using simulation. The feasibility of our approach is shown by a detailed case study, where the final output is three candidate architectures, representing different cost versus performance tradeoffs.
Membrane-distillation desalination : status and potential
(2004)

This paper presents an assessment of membrane distillation (MD) based on the available state of the art and on our preliminary analysis. The process has many desirable properties such as low energy consumption, ability to use low temperature beat, compactness, and perceivably more immunity to fouling than other membrane processes. Within the tested range, the operating parameters of conventional MD configurations have the following effects:(1) the permeate fluxes can significantly be improved by increasing the hot feed temperature (increasing the temperature from 50 to 70°C increases the flux by more than three-fold), and by reducing the vapor/air gap (reducing the vapor air gap thickness from 5 to 1 mm increase the flux 2.3-fold); (2) the mass flow rate of the feed solution has a smaller effect: increasing it three-fold increases the flux by about 1.3-fold; (3) the concentration of the solute has slight effect: increasing the concentration by more than five-fold decreases the flux by just 1.I 5-fold; (4) the cold side conditions have a lower effect (about half) on the flux than the hot side; (5) the coolant mass flow rate has a negligible effect; (6) the coolant temperature has a lower effect than the mass flow rate of the hot solution. Fouling effects, membranes used, energy consumption, system applications and configurations, and very approximate cost estimates are presented. The permeate fluxes obtained by the different researchers seem to disagree by an order of magnitude, and better experimental work is needed.
AutoModeling: Integrated Approach for Automated Model Generation by Ensemble Selection of Feature Subset and Classifier
(2018)

Feature subset selection and identification of appropriate classification method plays an important role to optimize the predictive performance of supervised machine learning system. Current literature makes isolated attempts to optimize the feature selection and classifier identification. However, feature set has an intrinsic relationship with classification technique and together they form a ‘model’ for classification task. In this paper, we propose AutoModeling that finds optimal learning model and jointly optimize the feature and hypothesis space to maximize performance measure objective function. It is an automated framework of selecting the ensemble model {selected feature subset, selected classifier} from a given superset of features and classifiers learned from given training dataset in a computational efficient manner. We introduce novel relax-greedy search with our proposed patience function as a wrapper feature selection that maximizes the predictive performance and eliminates the classical nesting effect. We perform extensive experimentations on different types of publicly available datasets and AutoModeling demonstrates superior performance over relevant state-of-the-art methods, expert-driven manual methods and deep neural networks.
The questionable contribution of medical measures to the decline of mortality in the United States in the twentieth century.
(1977)

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.
A new approach for robot motion planning using rapidly-exploring Randomized Trees
(2011)

In the last few years, car-like robots became increasingly important. Thus, motion planning algorithms for this kind of problem are needed more than ever. Unfortunately, this problem is computational difficult and so probabilistic approaches like Probabilistic Roadmaps or Rapidly-exploring Randomized Trees are often used in this context. This paper introduces a new concept for robot motion planning especially for car-like robots based on Rapidly-exploring Randomized Trees. In contrast to the conventional method, the presented approach uses a pre-computed auxiliary path to improve the distribution of random states. The main contribution of this approach is the significantly increased quality of the computed path. A proof-of-concept implementation evaluates the quality and performance of the proposed concept.
Detecting Predatory Behaviour in Game Chats
(2015)

While games are a popular social media for children, there is a real risk that these children are exposed to potential sexual assault. A number of studies have already addressed this issue, however, the data used in previous research did not properly represent the real chats found in multiplayer online games. To address this issue, we obtained real chat data from MovieStarPlanet, a massively multiplayer online game for children. The research described in this paper aimed to detect predatory behaviours in the chats using machine learning methods. In order to achieve a high accuracy on this task, extensive preprocessing was necessary. We describe three different strategies for data selection and preprocessing, and extensively compare the performance of different learning algorithms on the different datasets and features.
Using k-Poselets for Detecting People and Localizing Their Keypoints
(2014)

A k-poselet is a deformable part model (DPM) with k parts, where each of the parts is a poselet, aligned to a specific configuration of keypoints based on ground-truth annotations. A separate template is used to learn the appearance of each part. The parts are allowed to move with respect to each other with a deformation cost that is learned at training time. This model is richer than both the traditional version of poselets and DPMs. It enables a unified approach to person detection and keypoint prediction which, barring contemporaneous approaches based on CNN features, achieves state-of-the-art keypoint prediction while maintaining competitive detection performance.
Regularization for Unsupervised Deep Neural Nets
(2017)

Unsupervised neural networks, such as restricted Boltzmann machines (RBMs) and deep belief networks (DBNs), are powerful tools for feature selection and pattern recognition tasks. We demonstrate that overfitting occurs in such models just as in deep feedforward neural networks, and discuss possible regularization methods to reduce overfitting. We also propose a “partial” approach to improve the efficiency of Dropout/DropConnect in this scenario, and discuss the theoretical justification of these methods from model convergence and likelihood bounds. Finally, we compare the performance of these methods based on their likelihood and classification error rates for various pattern recognition data sets.
IEEE 802 . 11 ad : Directional 60 GHz Communication for Multi-Gbps
(2014)

With the ratification of the IEEE 802.11ad amendment to the 802.11 standard in December 2012, a major step has been taken to bring consumer wireless communication to the millimeter wave (mm-Wave) band. However, multi-Gbps throughput and small interference footprint come at the price of adverse signal propagation characteristics and require a fundamental rethinking of Wi-Fi communication principles. This paper describes the design assumptions taken into consideration for the IEEE 802.11ad standard and the novel techniques defined to overcome the challenges of mm-Wave communication. In particular we study the transition from omni-directional to highly directional communication and its impact on the design of
Scholarly communication and bibliometrics
(2002)

Why devote an ARIST chapter to scholarly communication and bibliometrics, and why now? Bibliometrics already is a frequently covered ARIST topic, with chapters such as that by White and McCain (1989) on bibliometrics generally, White and McCain (1997) on visualization of literatures, Wilson and Hood (2001) on informetric laws, and Tabah (2001) on literature dynamics. Similarly, scholarly communication has been addressed in other ARIST chapters such as Bishop and Star (1996) on social informatics and digital libraries, Schamber (1994) on relevance and information behavior, and many earlier chapters on information needs and uses. More than a decade ago, the first author addressed the intersection of scholarly communication and bibliometrics with a journal special issue and an edited book (Borgman, 1990; Borgman & Paisley, 1989), and she recently examined interim developments (Borgman, 2000a, 2000c). This review covers the decade (1990–2000) since the comprehensive 1990 volume, citing earlier works only when necessary to explain the foundation for recent developments. Given the amount of attention these topics have received, what is new and exciting enough to warrant a full chapter in 2001? What is new is that electronic scholarly communication is reaching critical mass, and CHAPTER 1
Improvements to BM25 and Language Models Examined
(2014)

Recent work on search engine ranking functions report improvements on BM25 and Language Models with Dirichlet Smoothing. In this investigation 9 recent ranking functions (BM25, BM25+, BM25T, BM25-adpt, BM25L, TF1°Δ°p×ID, LM-DS, LM-PYP, and LM-PYP-TFIDF) are compared by training on the INEX 2009 Wikipedia collection and testing on INEX 2010 and 9 TREC collections. We find that once trained (using particle swarm optimization) there is very little difference in performance between these functions, that relevance feedback is effective, that stemming is effective, and that it remains unclear which function is best over-all.
Age and Gender Estimation of Unfiltered Faces
(2014)

This paper concerns the estimation of facial attributes-namely, age and gender-from images of faces acquired in challenging, in the wild conditions. This problem has received far less attention than the related problem of face recognition, and in particular, has not enjoyed the same dramatic improvement in capabilities demonstrated by contemporary face recognition systems. Here, we address this problem by making the following contributions. First, in answer to one of the key problems of age estimation research-absence of data-we offer a unique data set of face images, labeled for age and gender, acquired by smart-phones and other mobile devices, and uploaded without manual filtering to online image repositories. We show the images in our collection to be more challenging than those offered by other face-photo benchmarks. Second, we describe the dropout-support vector machine approach used by our system for face attribute estimation, in order to avoid over-fitting. This method, inspired by the dropout learning techniques now popular with deep belief networks, is applied here for training support vector machines, to the best of our knowledge, for the first time. Finally, we present a robust face alignment technique, which explicitly considers the uncertainties of facial feature detectors. We report extensive tests analyzing both the difficulty levels of contemporary benchmarks as well as the capabilities of our own system. These show our method to outperform state-of-the-art by a wide margin.
Performance analysis of downlink NOMA networks over Rayleigh fading channels
(2018)

This paper analyzes the performance of downlink non-orthogonal multiple-access (NOMA) networks over independent but not necessarily identically distributed Rayleigh fading channels for arbitrary number of network users. Specifically, closed-form expressions for the average SNR, average achievable rate, and outage probability are derived, where it has been shown that network users achieve diversity orders equivalent to their ordered channel gains. Numerical evaluations are presented to validate the derived closed-form expressions for the different performance metrics, which are found to be in agreement with the network simulation results.
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
(2017)

Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions. Despite effectiveness, FM can be hindered by its modelling of all feature interactions with the same weight, as not all feature interactions are equally useful and predictive. For example, the interactions with useless features may even introduce noises and adversely degrade the performance. In this work, we improve FM by discriminating the importance of different feature interactions. We propose a novel model named Attentional Factorization Machine (AFM), which learns the importance of each feature interaction from data via a neural attention network. Extensive experiments on two real-world datasets demonstrate the effectiveness of AFM. Empirically, it is shown on regression task AFM betters FM with a 8.6% relative improvement, and consistently outperforms the state-of-the-art deep learning methods Wide&Deep [Cheng et al., 2016] and DeepCross [Shan et al., 2016] with a much simpler structure and fewer model parameters. Our implementation of AFM is publicly available at: https://github. com/hexiangnan/attentional factorization machine
Review on Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning
(2016)

Big Data Analytics and Deep Learning are two high-center of information science. Big Data has become important because of companies i.e. both public and private have been gathering huge measures of domain-specific data, which can contain helpful data about issues, for example, national intelligence, cyber security, fraud detection, marketing, and medical informatics. Deep Learning algorithms extract high-level, complex reflections as information representations through a progressive learning process. A advantage of Deep Learning is the study and learning of huge size of unsupervised information, making it a significant instrument for Big Data Analytics where crude information is to a great extent unlabeled and un-sorted. The present survey gives an idea of the previous work done by several researchers on the Big Data Analytics and Deep Learning applications.
Increasing ATM Efficiency withAssistant Based Speech Recognition
(2017)

Initiatives to integrate Automatic Speech Recognition into Air Traffic Management (ATM) exists at least since the late 90s. Some success to replace pseudo pilots have been reported, but its integration into controller assistant tools is missing. German Aerospace Center (DLR) and Saarland University developed Assistant Based Speech Recognition (ABSR) enabling command recognition rates better than 95%. However, good recognition rates are no convincing argument for decision makers. Therefore, we conducted an ABSR validation study with eight air traffic controllers to quantify the benefits with respect to workload and efficiency. The study validates that ABSR does not just reduce controllers’ workload, which would already be a lot, but this paper presents that ABSR significantly increases ATM efficiency. Fuel reductions of 60 liters (16 gallons) per flight and a throughput increase by two arrivals per hour are possible. Keywords—AcListant®, Automatic Speech Recognition (ASR), Assistant Based Speech Recognition (ABSR), ATM Efficiency, Electronic Flight Strips, Aircraft Label
The CoNLL 2007 Shared Task on Dependency Parsing
(2007)

The Conference on Computational Natural Language Learning features a shared task, in which participants train and test their learning systems on the same data sets. In 2007, as in 2006, the shared task has been devoted to dependency parsing, this year with both a multilingual track and a domain adaptation track. In this paper, we define the tasks of the different tracks and describe how the data sets were created from existing treebanks for ten languages. In addition, we characterize the different approaches of the participating systems, report the test results, and provide a first analysis of these results.
Dynamic adaptive streaming over HTTP -: standards and design principles
(2011)

In this paper, we provide some insight and background into the Dynamic Adaptive Streaming over HTTP (DASH) specifications as available from 3GPP and in draft version also from MPEG. Specifically, the 3GPP version provides a normative description of a Media Presentation, the formats of a Segment, and the delivery protocol. In addition, it adds an informative description on how a DASH Client may use the provided information to establish a streaming service for the user. The solution supports different service types (e.g., On-Demand, Live, Time-Shift Viewing), different features (e.g., adaptive bitrate switching, multiple language support, ad insertion, trick modes, DRM) and different deployment options. Design principles and examples are provided.
Dynamic Adaptive Streaming over HTTP – Design Principles and Standards
(2011)

In this paper, we provide some insight and background into the Dynamic Adaptive Streaming over HTTP (DASH) specifications as available from 3GPP and in draft version also from MPEG. Specifically, the 3GPP version provides a normative description of a Media Presentation, the formats of a Segment, and the delivery protocol. In addition, it adds an informative description on how a DASH Client may use the provided information to establish a streaming service for the user. The solution supports different service types (e.g., On-Demand, Live, Time-Shift Viewing), different features (e.g., adaptive bitrate switching, multiple language support, ad insertion, trick modes, DRM) and different deployment options. Design principles and some forward-looking considerations are provided.
Dummynet: a simple approach to the evaluation of network protocols
(1997)

Network protocols are usually tested in operational networks or in simulated environments. With the former approach it is not easy to set and control the various operational parameters such as bandwidth, delays, queue sizes. Simulators are easier to control, but they are often only an approximate model of the desired setting, especially for what regards the various traffic generators (both producers and consumers) and their interaction with the protocol itself.In this paper we show how a simple, yet flexible and accurate network simulator - dummynet - can be built with minimal modifications to an existing protocol stack, allowing experiments to be run on a standalone system. dummynet works by intercepting communications of the protocol layer under test and simulating the effects of finite queues, bandwidth limitations and communication delays. It runs in a fully operational system, hence allowing the use of real traffic generators and protocol implementations, while solving the problem of simulating unusual environments. With our tool, doing experiments with network protocols is as simple as running the desired set of applications on a workstation.A FreeBSD implementation of dummynet, targeted to TCP, is available from the author. This implementation is highly portable and compatible with other BSD-derived systems, and takes less than 300 lines of kernel code.